Tri-axial accelerometers quantify behaviour in the Eurasian badger (Meles meles): Towards an automated interpretation of field data

Abstract

Background: The popularity of tri-axial accelerometer data loggers to quantify animal activity through the analysis of signature traces is increasing. However, there is no consensus on how to process the large data sets that these devices generate when recording at the necessary high sample rates. In addition, there have been few attempts to validate accelerometer traces with specific behaviours in non-domesticated terrestrial mammals. We fitted a collar with a tri-axial accelerometer to a tame captive Eurasian badger (Meles meles). The animal was allowed to move freely in an outside enclosure and artificial sett whilst movements were recorded using a video camera. Data were analysed using custom-written software in terms of magnitude of movement, posture and periodicity using spectral analysis, a principal component analysis, the k-nearest neighbour algorithm and a decision tree to facilitate the automated classification of behaviours. Findings: We have demonstrated that various discrete behaviours (walking, trotting, snuffling and resting) can be differentiated using tri-axial accelerometer data. Classification accuracy ranged between 77.4% and 100% depending on the behaviour and classification method employed. Conclusions: These results are an important step in defining how accelerometer data code for the behaviour of free-ranging mammals. The classification methods outlined here have the potential to be used in the construction of a behavioural database and to generate behaviour-time budgets of hitherto unparalleled detail for wild animals. This would be invaluable for studies of nocturnal, subterranean or difficult-to-observe species that are particularly sensitive to human intrusion.